Dual Intelligent Prediction of Strength and Energy Absorption Performance of Rubber-Modified Concrete via Machine Learning and Metaheuristic Optimization Algorithms

This study presents a dual intelligent framework for predicting the uniaxial compressive strength (UCS) and energy transmission rate (ETR) of rubber-modified concrete, a promising aseismic material. An artificial neural network (ANN) was integrated with three advanced metaheuristic optimization algorithms, dream optimization algorithm (DOA), football optimization algorithm (FbOA), and hiking optimization algorithm (HOA), to enhance predictive accuracy. A database comprising 150 experimental results from UCS and ETR tests was used for model training and validation. Comparative evaluation revealed that the DOA-ANN model achieved the highest accuracy with a coefficient of determination (R2) of 0.9857, root mean square error (RMSE) of 0.9501, mean absolute error (MAE) of 0.5756, and variance accounted for (VAF) of 98.5716% for UCS prediction and R2 of 0.9708, RMSE of 1.5334, MAE of 0.9211, and VAF of 97.5063% for ETR prediction, outperforming other optimized ANN, random forest (RF), and conventional machine learning (ML) models. Shapley additive explanations (SHAP) analysis quantified feature importance, highlighting cement and specimen mass as critical predictors, while rubber content exhibited a dual role in strength reduction and energy absorption enhancement. A visual software tool embedding the optimal DOA-ANN model was further developed to enable rapid material design and real-time prediction. This work provides an efficient and interpretable artificial intelligence (AI)-driven approach for advancing the performance evaluation and design of sustainable aseismic concrete.